Author: E.Chin Purpose: Compare ASA and FFQ lactose. Will help guide how much FFQ analysis to do.

## [1] 346  15
## [1] 286  15
##  [1] "SubjectID"  "LCT"        "LNP"        "Ethnicity"  "age"       
##  [6] "sex"        "ht_cm"      "weightv2"   "bmi_final"  "bin_number"
## [11] "age_cat"    "bmi_cat"    "d_total"    "lacs"       "dt_kcal"
##  [1] "SubjectID"        "LCT"              "LNP"              "Ethnicity"       
##  [5] "age"              "sex"              "ht_cm"            "weightv2"        
##  [9] "bmi_final"        "bin_number"       "age_cat"          "bmi_cat"         
## [13] "Lactose.consumed" "D_TOTAL"          "KCAL"

Comparing ASA24 and FFQ data

283 subjects have both FFQ and ASA24 data

#who overlaps
gplots::venn(list("n FFQ subjects" = ffq$SubjectID, "n ASA subjects" = asa$SubjectID))

##  [1] "SubjectID"        "LCT"              "LNP"              "Ethnicity"       
##  [5] "age"              "sex"              "ht_cm"            "weightv2"        
##  [9] "bmi_final"        "bin_number"       "age_cat"          "bmi_cat"         
## [13] "d_total"          "lacs"             "dt_kcal"          "Lactose.consumed"
## [17] "D_TOTAL"          "KCAL"

Comparing Lactose

Plot of FFQ lactose (lacs) and ASA24 lactose (Lactose.consumed).
Color corresponds to LP status, size with bmi_final, and shape corresponds to sex (1 = Male, 2 = Female). Hover over the dots for more metadata info.

plot_ly(df, x = ~lacs, y = ~Lactose.consumed, 
        color = ~LNP, symbol = ~factor(sex), size = ~bmi_final,
        text = ~paste("SubjectID: ", SubjectID,
                      "<br>Ethnicity: ", Ethnicity,
                      "<br>LCT: ", LCT))
cor.test(df$lacs, df$Lactose.consumed, method = "pearson", alternative = "two.sided")
## 
##  Pearson's product-moment correlation
## 
## data:  df$lacs and df$Lactose.consumed
## t = 9.154, df = 281, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3841476 0.5643387
## sample estimates:
##       cor 
## 0.4792779

Comparing total dairy

Plot of FFQ total dairy (d_total, x axis) and ASA24 total dairy (D_TOTAL, y axis).
Color corresponds to LP status, size with bmi_final, and shape corresponds to sex (1 = Male, 2 = Female). Hover over the dots for more metadata info.

plot_ly(df, x = ~d_total, y = ~D_TOTAL, 
        color = ~LNP, symbol = ~factor(sex), size = ~bmi_final,
        text = ~paste("SubjectID: ", SubjectID,
                      "<br>Ethnicity: ", Ethnicity,
                      "<br>LCT: ", LCT))
cor.test(df$d_total, df$D_TOTAL, method = "pearson", alternative = "two.sided")
## 
##  Pearson's product-moment correlation
## 
## data:  df$d_total and df$D_TOTAL
## t = 9.2523, df = 281, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3885195 0.5678309
## sample estimates:
##       cor 
## 0.4832267

Compare total caloric intake

Plot of FFQ total intake (dt_kcal, x axis) and ASA24 total intake (KCAL, y axis).
Color corresponds to LP status, size with bmi_final, and shape corresponds to sex (1 = Male, 2 = Female). Hover over the dots for more metadata info.

plot_ly(df, x = ~dt_kcal, y = ~KCAL, 
        color = ~LNP, symbol = ~factor(sex), size = ~bmi_final,
        text = ~paste("SubjectID: ", SubjectID,
                      "<br>Ethnicity: ", Ethnicity,
                      "<br>LCT: ", LCT))
cor.test(df$dt_kcal, df$KCAL, method = "pearson", alternative = "two.sided")
## 
##  Pearson's product-moment correlation
## 
## data:  df$dt_kcal and df$KCAL
## t = 8.8866, df = 281, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3721096 0.5546898
## sample estimates:
##       cor 
## 0.4683853

Compare FFQ total dairy to FFQ lactose

Plot of FFQ total dairy (d_total, x axis) and FFQ lactose (lacs, y axis).
Color corresponds to LP status, size with bmi_final, and shape corresponds to sex (1 = Male, 2 = Female). Hover over the dots for more metadata info.

plot_ly(df, x = ~d_total, y = ~lacs, 
        color = ~LNP, symbol = ~factor(sex), size = ~bmi_final,
        text = ~paste("SubjectID: ", SubjectID,
                      "<br>Ethnicity: ", Ethnicity,
                      "<br>LCT: ", LCT))
cor.test(df$d_total, df$lacs, method = "pearson", alternative = "two.sided")
## 
##  Pearson's product-moment correlation
## 
## data:  df$d_total and df$lacs
## t = 27.707, df = 344, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7952342 0.8609924
## sample estimates:
##      cor 
## 0.830994